Responsible for coding and testing customized analytical models of medium to high degree of complexity.
Must be able to implement best practices when writing and testing code.
Familiarity with the life cycle of a data science project and extensive experience with python is a must.
Requires strong interpersonal and communication skills, operational Elasticsearch knowledge and ability to construct GUI interfaces
Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business.
Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs
Proven experience in developing models using Regression, Decision trees, Bayesian networks, Random Forest, Logistic regression, Support vector machine, Gradient boosting algorithms, Clustering algorithms and Dimensionality Reduction Algorithms.
Proven experience in building and implementing Timeseries and forecasting models.
Experience in Deep Learning model development using TensorFlow, Keras, PyTorch
Hands on experience in building ML workflows
Experience in Model implementation, Governance, and monitoring.
Responsibilities include Performing the code development of complex analytic algorithms and paradigms, perform a clear breakdown of the analytic functionality and separate it into working modules.
Working experience in Python, R and SQL
Experience in Python data science packages such as numPy, SciPy and SciKit-learn
Nice to Have:
Fluent with Natural Language Processing and text analytics libraries (NLTK, SpaCy)
Experience with Data Science Platforms (Dataiku, H2o, Azure…)
Experience with one of Subversion/SVN, Git, GitHub, GitLab, Mercurial, or other version control system
Experience with cloud platforms AWS, Azure, GCP a plus
Knowledge in Big Data, Hadoop, HIVE/HQL, HDFS, Spark/PySpark, Kafka